Shadow AI Assessment

Your employees are already using AI. They're uploading spreadsheets to ChatGPT for analysis. They're pasting customer communications into Claude for drafting responses. They're using Copilot to write code faster. They're doing this because these tools solve problems your official systems don't, and they're not waiting for IT to catch up.

Every one of those unauthorized uses is a signal. A spreadsheet uploaded to ChatGPT is a feature request for better analytics. A document pasted into an LLM for summarization is a feature request for a document processing pipeline. A personal Copilot installation is a feature request for developer productivity tooling.

We start with discovery. Through interviews, surveys, and where possible, usage auditing, we map what tools your people are actually using, what workflows they're applying them to, what data they're exposing, and what official systems they're routing around.

Next, we rank every identified workflow by business value and feasibility. Business value measures time saved, number of people affected, and connection to a business outcome. Feasibility measures how hard it would be to build an approved version: does the technology exist, does the data pipeline exist, can it meet your security requirements.

The top three workflows become detailed deliverables. For each, we provide a build-vs-buy analysis: can you solve this with a commercial tool your security team can approve, or does it require internal development? Each analysis includes estimated cost, timeline, data requirements, and the compliance controls needed.

The final deliverable is a specification your engineering team can execute. Inputs, outputs, integration points, governance requirements. Concrete enough to scope a build but not so prescriptive that it constrains your team's choices.

This isn't a compliance audit looking for people to punish. It's a demand signal analysis. Your employees wrote your AI roadmap with their workarounds. We help you read it.